This is the age of Agentic AI.

Truly autonomous AI agents are capable of reshaping operations entirely. Systems can act autonomously, make decisions, and adapt dynamically. These agents will go beyond conversational interfaces, responding to user input and proactively managing tasks, navigating complex IT environments, and orchestrating business processes.

However, this shift isn’t just about technology — it also comes with a few considerations. Companies will need to address regulatory challenges, build AI literacy, and focus on applied use cases with clear ROI if the evolution is to succeed.

Moving from acceleration to transformation

So far, companies have primarily used AI to accelerate existing processes, whether through chatbots improving customer interactions or AI-driven analytics optimising workflows. In the end, these implementations make businesses more efficient.

But acceleration alone is no longer enough to stay ahead in the game. The real opportunity lies in replacing outdated workflows entirely and creating new, previously impossible capabilities.

For example, AI plays a vital role in automating troubleshooting and enhancing security within the network industry. But what if AI could autonomously anticipate and predict failures, reconfigure networks proactively to avoid service level degradations in real time, and optimise performance without human intervention? As AI becomes more autonomous, its ability to not just assist but act independently will be key to unlocking new levels of productivity and innovation.

That’s what Agentic AI is about.

Navigating the AI regulatory landscape

However, as AI becomes more autonomous, the regulatory landscape governing its deployment will evolve in parallel. The introduction of the EU AI Act, alongside global regulatory frameworks, means companies must already navigate new compliance requirements related to AI transparency, bias mitigation, and ethical deployment.

That means AI governance can no longer be an afterthought.

AI-powered systems must be designed with built-in compliance mechanisms, data privacy protections, and explainability features to build trust among users and regulators alike. Zero-trust security models will also be crucial in mitigating risks, enforcing strict access controls, and ensuring that AI decisions remain auditable and secure.

The importance of AI literacy

As stated, the success of Agentic AI’s era will depend on more than just technical capabilities – it will require alignment between leadership, developers, and end-users. As AI becomes more advanced, AI literacy becomes a key differentiator, and companies must invest in upskilling their workforce to understand AI’s capabilities, limitations, and ethical considerations. A recent report by the ICT Workforce Consortium found that 92% of information and communication technology jobs are expected to undergo significant transformation due to advancements in AI. So, without proper AI education, businesses risk misalignment between AI implementers and those who use the technology.

This can lead to a lack of trust, slow adoption, and ineffective deployment, which can impact the bottom line. So, to unlock the full potential of Agentic AI, it’s essential to build AI literacy across all levels of the organisation.

As this new era of AI blooms, companies must learn from the current era of AI adoption: focus on applied use cases with tangible ROI. The days of experimenting with AI for innovation’s sake are ending – the next generation of AI deployments must prove their worth.

In networking, it could be projects such as AI-powered autonomous network optimisation. These systems do more than automate tasks; they continuously monitor network traffic, predict congestion points, and autonomously adjust configurations to ensure optimal performance. By providing proactive insights and real-time adjustments, these AI-driven solutions help companies prevent issues and outages before they occur.

This level of AI autonomy reduces human intervention and enhances overall security and operational efficiency.

Identifying and implementing high-value, high-impact Agentic AI use cases such as these will be vital.

Trust as the adoption hurdle

While we’re entering a new era, trust plays a key role in widespread AI adoption. Users must feel confident that AI decisions are accurate, fair, and explainable. Even the most advanced AI models will face challenges gaining acceptance without transparency.

This is particularly relevant as AI transitions from assisting users to making autonomous decisions. Whether AI agents manage IT infrastructure or drive customer interactions, organisations must ensure that AI decisions are auditable, unbiased, and aligned with business objectives.

Without transparency and accountability, companies may face resistance from both employees and customers.

The future of AI

Looking ahead, 2025 holds exciting potential for AI. As it reaches a new level of maturity, its success will depend on how well organisations, governments, and individuals adapt to its growing presence in everyday life. Moving beyond efficiency and automation, AI has the opportunity to become a powerful driver of intelligent decision-making, problem-solving, and innovation.

Organisations that harness Agentic AI effectively – balancing autonomy with oversight – will see the greatest benefits. However, success will require a commitment to transparency, education, and ethical deployment to build trust and ensure AI is a true enabler of progress.

Because AI is no longer just an accelerant, it is a transformative force reshaping how we work, communicate, and interact with technology.

Photo by Ryan De Hamer on Unsplash

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Humans have travelled the sea in some form for tens of thousands of years—with the earliest crossings occurring around 53,000 to 65,000 years ago (when Australo-Melanesian populations migrated into the Sahul landmass – known today as Australia and New Guinea – from what used to be the Sundaland peninsula.)

It’s often said how we know more about the moon than our oceans, with around 95 percent still unexplored. Arguably, the last major ocean research expedition was between 1872 and 1876 when a converted Royal Navy gunship known as the Challenger travelled close to 70,000 nautical miles and catalogued over 4,000 previously unknown species.

Inspired by the Challenger’s story, IBM has teamed up with non-profit ProMare to make a similarly large impact on ocean research.

The autonomous ship, Mayflower, is named after the ship which carried pilgrim settlers from Plymouth, England to Plymouth, Massachusetts in 1620. On its 400th anniversary, it was decided that a Mayflower for the 21st century should be built.

Brett Phaneuf, a Founding Board Member of ProMare and Co-Director of the Mayflower Autonomous Ship project, said:

“Putting a research ship to sea can cost tens of thousands of dollars or pounds a day and is limited by how much time people can spend onboard – a prohibitive factor for many of today’s marine scientific missions.

With this project, we are pioneering a cost-effective and flexible platform for gathering data that will help safeguard the health of the ocean and the industries it supports.”

Naturally, there are more than a few differences between the original ship and the Mayflower Autonomous Ship (MAS).

Mayflower 2.0 no longer relies solely on wind power and will use a wind/solar hybrid propulsion system with a backup diesel generator. The new ship also trades in a compass and nautical charts for navigation in favour of a state-of-the-art GNSS positioning system with SATCOM, RADAR, and LIDAR.

IBM’s deep learning technology is on-board to help the ship traverse the harsh and rapidly-changing environment of the ocean.

Donald Scott, Director of Engineering at Marine AI (which partnered with ProMare on the project), explained:

“In the middle of the ocean, communications are severely limited. Conditions can change very suddenly, and you don’t have the option to stop and power down.

With MAS, we needed to go beyond the existing technology for unmanned ships, creating a vessel that isn’t just operated remotely and doesn’t simply react to the environment, but learns and adapts independently.

To do this, we had to develop state-of-the-art capabilities around navigation, collision avoidance, communications and more.”

The training of AI models for the MAS began in October 2019. The actual hull for the ship arrived in Plymouth in March and sea trials began. Over the next few months, the ship was fitted with its advanced navigation and research equipment.

Andy Stanford-Clark, CTO of IBM UK & Ireland, added:

“IBM helped put man on the moon and is excited by the challenge of using advanced technologies to cross and research our deepest oceans.

By providing the brains for the Mayflower Autonomous Ship, we are pushing the boundaries of science and autonomous technologies to address critical environmental issues.”

MAS’ voyage couldn’t arrive at a more needed time with humans causing huge amounts of damage to the health of our oceans. A UN report found our oceans are now warmer, more polluted, more depleted, and more acidic than ever before.

Rising sea levels are among the key concerns about the impact on humans, but another is the increasing number of plastics in the sea which is simultaneously causing harm to sealife and ending up in the food we eat.

Professor Richard Thompson, OBE, Director of the Marine Institute, University of Plymouth, commented:

“Microplastics present a substantial challenge to our oceans. Over 700 species come into contact with marine litter which is found from the poles to the equator, and estimates are that the quantity of plastic in the oceans will triple in the decade to 2025.”

However, armed with the right data, it’s not too late to change course and heal our oceans.

MAS is fitted with a range of sensors including acoustic, nutrient, temperature, and water and air samplers. Edge devices will store and analyse all data locally until connectivity is available. When a link has been established, the data will be uploaded to edge nodes onshore.

Unless there are any last-minute delays, MAS is set to depart on its voyage this month. The ship is due to arrive in Plymouth, Massachusetts around two weeks later. Where required, updated deep learning models can be pushed out to the ship.

MAS’ virtual crew will be based in Plymouth, UK but IBM says millions of virtual “pilgrims” will be able to experience the voyage online.

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Speaking to investors and media at Volvo’s annual event for the capital-markets community, Volvo Group CEO Martin Lundstedt said:

“Partnership is the new leadership. If we are to succeed in the future with speed, quality, and safety – and to gain benefits of autonomous driving – we need to partner up with the best guys. In this world of unknowns, you need a partnership built on trust.”

Volvo is the world’s second-largest truckmaker after Daimler. The carmaker demonstrated its first cabin-less autonomous truck called Vera last year.

By applying AI to trucks, Volvo hopes to disrupt industries from public and freight transport, to forestry and construction.

Jensen Huang, NVIDIA founder and CEO, commented:

“Trucking is the world’s largest network – a network that through online shopping puts practically anything, anywhere in the world, quickly within our reach.

The latest breakthroughs in AI and robotics bring a new level of intelligence and automation to address the transportation challenges we face. We are thrilled to partner with Volvo Group to reinvent the future of trucking.”

The deal with Volvo is a particular boost for Nvidia and its AI technology for self-driving cars after Tesla ditched the firm last year. Tesla CEO Elon Musk said the company would focus on creating its own chips.

Nvidia debuted its ‘Xavier’ processors for the company’s DRIVE autonomous car platform last year. Xavier was in development for over four years, represents the work of over 2,000 engineers, features more than nine billion transistors, and Nvidia claims it’s the most complex system-on-a-chip (SoC) ever created

During a conference call in August last year, Huang said: “It’s super hard to build Xavier and all the software stack on top of it. If it doesn’t turn out for whatever reason for them [Tesla] you can give me a call and I’d be more than happy to help.”

Nvidia has made a name for itself as the maker of powerful GPUs, traditionally for gaming purposes. The company has been increasingly shifting gears into other computation-heavy areas like AI and machine learning.

Automotive chips accounted for $641 million of Nvidia’s $11.7 billion in revenue in its most recent fiscal year.

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